Update readme (#11357)

README.md

@@ -6,7 +6,7 @@
 # 💫 Intel® LLM library for PyTorch*
 **`IPEX-LLM`** is a PyTorch library for running **LLM** on Intel CPU and GPU *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)* with very low latency[^1]. 
 > [!NOTE]
-> - *It runs on top of Intel Extension for PyTorch (**`IPEX`**), and is built on top of the excellent work of **`llama.cpp`**, **`transformers`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
+> - *It is built on top of the excellent work of **`llama.cpp`**, **`transformers`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
 > - *It provides seamless integration with [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), [Ollama](https://ipex-llm.readthedocs.io/en/main/doc/LLM/Quickstart/ollama_quickstart.html), [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [HuggingFace transformers](python/llm/example/GPU/HF-Transformers-AutoModels), [LangChain](python/llm/example/GPU/LangChain), [LlamaIndex](python/llm/example/GPU/LlamaIndex), [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP), [vLLM](python/llm/example/GPU/vLLM-Serving), [FastChat](python/llm/src/ipex_llm/serving/fastchat), [Axolotl](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html), [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning), [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO), [AutoGen](python/llm/example/CPU/Applications/autogen), [ModeScope](python/llm/example/GPU/ModelScope-Models), etc.* 
 > - ***50+ models** have been optimized/verified on `ipex-llm` (including LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, RWKV, and more); see the complete list [here](#verified-models).*
 
@@ -116,6 +116,14 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
 </table>
 -->
 
+## Model Accuracy
+Please see the **Perplexity** result below (tested on Wikitext dataset using the script [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/dev/benchmark/perplexity)).
+|Perplexity                 |sym_int4	|q4_k	  |fp6	  |fp8_e5m2 |fp8_e4m3 |fp16   |
+|---------------------------|---------|-------|-------|---------|---------|-------|
+|Llama-2-7B-chat-hf	        |6.3638	  |6.2179	|6.0924	|6.1796	  |6.0980   |6.0963 | 
+|Mistral-7B-Instruct-v0.1	  |6.0025	  |5.9581	|5.8930	|5.8884	  |5.8820   |5.8734 | 
+|Qwen1.5-7B-chat	           |8.8652	  |8.8163	|8.5573	|8.8463	  |8.5304   |8.6065 | 
+
 ## Latest Update 🔥 
 - [2024/05] You can now easily run `ipex-llm` inference, serving and finetuning using [Docker](#docker).
 - [2024/05] You can now install `ipex-llm` on Windows using just "*[one command](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html#install-ipex-llm)*".

docs/readthedocs/source/conf.py

@@ -77,8 +77,8 @@ source_suffix = {'.rst': 'restructuredtext',
 master_doc = 'index'
 
 project = 'IPEX-LLM'
-copyright = '2024, IPEX-LLM Authors'
+#copyright = '2024, BigDL Authors'
-author = 'IPEX-LLM Authors'
+author = 'BigDL Authors'
 
 # The short X.Y version
 #version = ''

docs/readthedocs/source/index.rst

@@ -30,7 +30,7 @@
        <p>
          <ul>
             <li><em>
-               It runs on top of Intel Extension for PyTorch (<strong><code><span>IPEX</span></code></strong>), and is built on top of the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>transfromers</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc. 
+               It is built on top of the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>transfromers</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc. 
             </li></em>
             <li><em>
                It provides seamless integration with <a href=doc/LLM/Quickstart/llama_cpp_quickstart.html>llama.cpp</a>, <a href=doc/LLM/Quickstart/ollama_quickstart.html>ollama</a>, <a href=doc/LLM/Quickstart/webui_quickstart.html>Text-Generation-WebUI</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels>HuggingFace transformers</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning>HuggingFace PEFT</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LangChain >LangChain</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LlamaIndex >LlamaIndex</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Deepspeed-AutoTP >DeepSpeed-AutoTP</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/vLLM-Serving >vLLM</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/src/ipex_llm/serving/fastchat>FastChat</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning/DPO>HuggingFace TRL</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/Applications/autogen >AutoGen</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/ModelScope-Models >ModeScope</a>, etc.